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ESTRO 35 2016 S971
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preterm stop of the therapy thus jeopardizing the intended
treatment outcome. Despite numerous research attempts
there is still no robust feature established in clinical routine
to predict radiotherapy-induced toxicity prior to therapy
start.
Material and Methods:
The study cohort comprised 40
patients who underwent neoadjuvant radiochemotherapy (N-
RCT) for rectal cancer (28x1.8 Gy, 5 times weekly,
concomitant with two cycles 5-FU-based chemotherapy).
From each of those patients dermal fibroblasts were cultured
from skin specimen gained outside of the radiotherapy
planning target volume at occasion of surgery conducted
about six weeks upon N-RCT completion. Acute radiotoxicity
was thoroughly monitored throughout the N-RCT series and
documented according to CTC classification. Maximal acute
toxicity (MAT) was defined by the highest CTC grade of the
four items “cystitis”, “proctitis”, “enteritis”, and
“dermatitis”. MAT was grouped into grades 0/1 (n=16), 2
(n=16), and 3/4 (n=8). N-RCT was simulated in the cultured
fibroblasts for five consecutive days (1.8 Gy each at d1-d5
with addiation of 5-FU at a concentration reflecting clinical
steady-state levels) followed by a 7-day wash-out period.
Gene expression of nine candidate genes (
CAT
,
CDKN1A
,
CTGF
,
SMAD2/3/4/7
,
TGFB1
,
TGFBR1
) supposed to mediate
early radiation-induced toxicity was ascertained by
quantitative real time PCR. Samples for these RNA analyses
were harvested at d2 and d5 (each 4 hours upon application
of the radiation fraction) as well as at day 12 upon the wash-
out period.
GAPDH
and
HPRT1
transcript levels served as
reference.
Results:
MAT was related to radiation-induced expression
changes of four of the considered genes in fibroblasts. The
strongest impact was obtained for
SMAD7
and
CAT
at d5. The
higher the MAT score, the lower the induction of
SMAD7
and
CAT
by radiation was (p=0.001 and 0.003). However, upon
the wash-out period at d12 no statistical differences in
dependence on the MAT score were seen anymore for these
two genes. In contrast, a high MAT score was linked to low
radiation-induced induction of
CTGF
(p=0.005) and to a faster
decrease of the massively induced
CDKN1A
(p=0.03) at d12.
At d2, a trend (p=0.06) for
CAT
in relation to MAT in the
same direction as at d5 was noticed with no correlation of
any of the other genes at this early time point.
Conclusion:
Radiation-induced expression changes in patient-
matched fibroblasts may serve as biomarkers to predict
clinical radiotoxicity. Induction of
SMAD7
and
CAT
may
mitigate TGFbeta signalling and reactive oxygen species load
thus saving normal tissue during radiotherapy. A protective
role might also be attributed to sustained elevation of
CDKN1A
. The link between post-radiation induction of
CTGF
in fibroblasts and low MAT remains to be clarified.
Understanding the mechanistic basis of these findings might
pave the way for better protection of irradiated normal
tissue.
EP-2058
A novel multi-SNP model predictive of erectile dysfunction
following radiotherapy in prostate cancer
J.H. Oh
1
Memorial Sloan Kettering Cancer Center, Department of
Medical Physics, New York, USA
1
, S. Kerns
2
, H. Ostrer
3
, B. Rosenstein
4
, J.O. Deasy
1
2
University of Rochester Medical Center, Department of
Radiation Oncology, Rochester, USA
3
Albert Einstein College of Medicine, Departments of
Pathology and Genetics, New York, USA
4
Mount Sinai School of Medicine, Department of Radiation
Oncology, New York, USA
Purpose or Objective:
Erectile dysfunction (ED) is one of the
most common complications encountered after radiotherapy
in prostate cancer patients. The goal of this study was to
investigate whether single nucleotide polymorphisms (SNPs)
are associated with late ED in men treated with radiotherapy
for prostate cancer. To this end, we designed a novel
machine learning-based multi-SNP model using a genome-
wide association study (GWAS) dataset.
Material and Methods:
We analyzed 236 evaluable patients
with at least one year of follow-up for the development of
ED. The severity of ED was assessed using either the patient-
administered Sexual Health Inventory for Men (SHIM) or the
clinician-assigned Mount Sinai Erectile Function (MSEF)
scoring schema. There were 133 patients with Grade 2 or
more ED. For our analysis, the cohort was split into two
groups (cases/controls: MSEF 0,1 / 2,3; cases/controls: SHIM
≤7 / ≥16). Genome-wide SNP data were available from
Affymetrix Genome-Wide Human SNP Array 6.0. After a
quality test including SNP missing rate>5%, minor allele
frequency (MAF)<5%, and Hardy-Weinberg equilibrium
(p<10E-5), 613,496 SNPs remained.
For the validation purpose of our proposed model, the
dataset was split into a training dataset (2/3 of samples) and
a validation dataset (1/3 of samples). Our model building
process was performed using the bootstrapped data from the
training dataset. Our idea is to convert the binary outcomes
into preconditioned continuous outcomes based on normal
tissue complication probability (NTCP) using principal
component analysis (PCA) and logistic regression. The
preconditioned outcomes were used in the model building
process using random forest regression. Then, the model was
tested using the validation dataset. The final predicted
outcomes were compared with the original binary outcomes
to estimate the predictive performance. We iterated this
process 100 times and the performance was averaged. We
compared the performance of our proposed method
(preconditioning random forest regression: PRFR) with other
methods including preconditioning lasso (PL), lasso logistic
regression (LLR), and random forest classification (RFC).
Results:
Univariate analysis was performed using the training
dataset. With a threshold of p=0.001, 367 SNPs remained.
These SNPs were fed into our model. As shown in Figure (A),
the averaged performance with the validation dataset was
AUC=0.62, which is better than other methods: RFC (0.57),
PL (0.58), and LLR (0.57). The 79 patients in the validation
dataset were binned into 6 groups according to the predicted
risk of ED. Figure (B) shows the comparison of the model-
predicted incidence of ED with observed incidence.
Conclusion:
Our machine learning-based multi-SNP model
showed the potential to better predict the radiation-induced
late ED. However, we need to validate our model using other
datasets.
EP-2059
Changes in hypoxia in serial F-MISO/PET-CT during
chemoradiation in HNSCC
H. Kerti
1
University Hospital Freiburg, Department of Radiation
Oncology, Freiburg, Germany
1
, A. Bunea
1
, L. Majerus
1
, M. Mix
2
, C. Stoykow
2
, N.
Wiedenmann
1
, P.T. Meyer
2
, A.L. Grosu
1
2
University Hospital Freiburg, Department of Nuclear
Medicine, Freiburg, Germany
Purpose or Objective:
Tumor hypoxia, a common feature of
locally advanced head and neck cancer (HNSCC), is
associated with higher malignancy and increased
radioresistance. The decrease of tumor hypoxia during